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Sparse Ultrasound Imaging via Manifold Low-Rank Approximation and Non-Convex Greedy Pursuit
Thiago Alberto Rigo Passarin1, Marcelo Victor Wüst Zibetti2, Daniel Rodrigues Pipa3
1Graduate Program in Electrical and Computer Engineering (CPGEI), Federal University of Technology, Paraná (UTFPR), Curitiba PR 80230-901, Brazil. passarin@utfpr.edu.br.
Sensors (Basel, Switzerland)
|November 28, 2018
Summary
This study introduces a novel image reconstruction method for ultrasound, overcoming off-grid deviations by approximating continuous scatterer locations. The technique enhances accuracy in sparse image reconstruction from noisy data.
Area of Science:
- Medical Imaging
- Signal Processing
- Computational Physics
Background:
- Model-based image reconstruction enhances MRI and ECT resolution.
- Existing methods fail in ultrasound due to discrete grid assumptions, causing off-grid deviation.
- Real-world objects have continuous scatterer locations, not discrete grids.
Purpose of the Study:
- To develop a novel image reconstruction method for pulse-echo ultrasound.
- To address and overcome the challenge of off-grid deviation in ultrasound imaging.
- To improve accuracy and resolution in ultrasound image reconstruction.
Main Methods:
- Dictionary expansion using coherent sampling and rank reduction.
- Constrained reconstruction approximating continuous scatterer locations.
- A greedy algorithm based on Orthogonal Matching Pursuit with non-convex constraints.
Main Results:
- Successful reconstruction of sparse images from noisy simulated 2D ultrasound data.
- Demonstrated higher accuracy compared to traditional discrete models.
- Method allows for flexible region of interest cell division.
Conclusions:
- The proposed method effectively handles the continuous nature of scatterer locations in ultrasound.
- This approach offers improved performance for ultrasound image reconstruction, particularly in nondestructive testing.
- The technique provides a more accurate representation of the imaging medium.
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